We can select elements and subarrays from NumPy arrays using the standard square bracket notation that we use with Python lists.
\nAlong a single axis, we use integers to select single elements.
\nWe can use -ve integers to index elements from the end of an array. The last element is indexed with -1, the second last element is indexed with -2 and so on.
\nimport numpy as np\n\ndata = np.arange(0, 10, 2)\n\ndata\narray([0, 2, 4, 6, 8])\n\ndata[1] # the second element, at index 1\n# 2\n\ndata[-1] # the last element\n# 8\n\ndata[2]\n# 4We can select a sequence of elements using slices, which are specified using the colon (:) notation.
a[x:y] - Select elements with index starting at x and ending at y-1. (x & y are integers.). The slice a[x:y] can also be written as a[x:y:1], where the number 1 specifies that every element between x & y should be selected. To select every second element between x & y use a[x:y:2].
a[:] - Select all the elements in the given axis.
\na[:x ] - Select elements starting with 0 and going up to index x-1 (integer).
\na[x:] - Select elements starting from index x (integer) and going up to the last element in the array.
\na[::-1] - Select all elements in the reverse order.
\nimport numpy as np\n\ndata = np.arange(0, 10, 2)\n\ndata\n# array([0, 2, 4, 6, 8])\n\ndata[1:3]\n# array([2, 4])\n\ndata[:4]\n# array([0, 2, 4, 6])\n\ndata[:]\n# array([0, 2, 4, 6, 8])We can extract columns and rows from a two-dimensional array using a combination of slices and integer indexing.
\nimport numpy as np\n\ndata = np.array([[1, 4, 7, 8, 3], [10, 45, 24, 39, 87], [23, 44, 78, 19, 39], [74, 38, 26, 38, 45]])\n\ndata\n# array([[ 1, 4, 7, 8, 3],\n# [10, 45, 24, 39, 87],\n# [23, 44, 78, 19, 39],\n# [74, 38, 26, 38, 45]])\n\ndata[:, 2] # select the third column\n# array([ 7, 24, 78, 26])\n\ndata[2, :] # select the third row\n# array([23, 44, 78, 19, 39])We can extract subarrays by applying a slice on each of the array dimensions.
\ndata[1:, :2]\n# array([[10, 45],\n# [23, 44],\n# [74, 38]])